Autonomous driving method and apparatus

ABSTRACT

The present disclosure provides an autonomous driving method and an apparatus. The method includes: receiving a currently collected image transmitted by a unmanned vehicle, where the currently collected image is an image collected in a target scenario; acquiring current driving data according to the currently collected image and a pre-trained autonomous driving model, where the autonomous driving model is used to indicate a relationship between an image and driving data in at least two scenarios, and the at least two scenarios include the target scenario; and sending the current driving data to the unmanned vehicle. Robustness of the unmanned driving method is improved.

CROSS-REFERENCE TO RELATED DISCLOSURE

This application claims priority to Chinese Patent Disclosure No.201811633970.X, filed on Dec. 29, 2018, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the field of autonomous drivingtechnologies and, in particular, to an autonomous driving method and anapparatus.

BACKGROUND

An autonomous vehicle is also known as an unmanned vehicle, acomputer-driven vehicle or a wheeled mobile robot, which is anintelligent vehicle that achieves unmanned driving through a computersystem. Mainstream methods for achieving autonomous driving include aperception-decision-control method and an end-to-end method. Where theend-to-end method needs to achieve the purpose of autonomous drivingthrough processes such as data collecting and model training.

In the prior art, a model obtained from the end-to-end method is subjectto data that is collected. When the collected data is data collected ina certain scenario, a model obtained from training based on the data canonly be used in this scenario, and if the autonomous vehicle is inanother scenario, the model cannot produce appropriate output. It can beseen that the autonomous driving method provided in the prior art has alimited application scenario and low robustness.

SUMMARY

The present disclosure provides an autonomous driving method and anapparatus for solving the problem of low robustness in the prior art.

In a first aspect, the present disclosure provides an autonomous drivingmethod, including:

receiving a currently collected image sent by an unmanned vehicle, wherethe currently collected image is an image collected in a targetscenario;

acquiring current driving data according to the currently collectedimage and a pre-trained autonomous driving model, where the autonomousdriving model is used to indicate a relationship between an image anddriving data in at least two scenarios, and the at least two scenariosinclude the target scenario; and

sending the current driving data to the unmanned vehicle.

Optionally, before the acquiring current driving data according to thecurrently collected image and a pre-trained autonomous driving model,the method includes:

generating a sample image of the target scenario according to a sampleimage of a known scenario; and

acquiring the autonomous driving model according to the sample image ofthe known scenario and the sample image of the target scenario.

Optionally, the generating a sample image of the target scenarioaccording to a sample image of a known scenario includes:

acquiring the sample image of the target scenario according to thesample image of the known scenario and a pre-trained domain conversionmodel, where the domain conversion model is used to indicate arelationship between an image of the known scenario and an image of thetarget scenario.

Optionally, the sample image of the known scenario is a daytime sampleimage, and the sample image of the target scenario is a night sampleimage; and

the acquiring the sample image of the target scenario according to thesample image of the known scenario and a pre-trained domain conversionmodel includes:

acquiring the night sample image according to the daytime sample imageand the pre-trained domain conversion model, where the domain conversionmodel is used to indicate a relationship between a daytime image and anight image.

Optionally, the sample image of the known scenario is a sunny-day sampleimage, and the sample image of the target scenario is a rainy-day sampleimage; and

the acquiring the sample image of the target scenario according to thesample image of the known scenario and a pre-trained domain conversionmodel includes:

acquiring the rainy-day sample image according to the sunny-day sampleimage and the pre-trained domain conversion model, where the domainconversion model is used to indicate a relationship between a sunny-dayimage and a rainy-day image.

Optionally, before the acquiring the sample image of the target scenarioaccording to the sample image of the known scenario and a pre-traineddomain conversion model, the method further includes:

training a model with a generative adversarial network (GAN) techniqueto obtain the domain conversion model.

Optionally, the sample image of the known scenario is a sample image inwhich there is no pedestrian and vehicle in front of a vehicle, and thesample image of the target scenario is a sample image in which there isa pedestrian and a vehicle in front of the vehicle; and

the generating a sample image of the target scenario according to asample image of a known scenario includes:

performing vehicle marking and pedestrian marking on the sample image inwhich there is no pedestrian and vehicle in front of the vehicle toobtain the sample image in which there is a pedestrian and a vehicle infront of the vehicle.

In a second aspect, the present disclosure provides an autonomousdriving apparatus, including:

a receiving module, configured to receive a currently collected imagesent by an unmanned vehicle, where the currently collected image is animage collected in a target scenario;

an acquiring module, configured to acquire current driving dataaccording to the currently collected image and a pre-trained autonomousdriving model, where the autonomous driving model is configured toindicate a relationship between an image and driving data in at leasttwo scenarios, and the at least two scenarios include the targetscenario; and

a sending module, configured to send the current driving data to theunmanned vehicle.

Optionally, the acquiring module is further configured to:

generate a sample image of the target scenario according to a sampleimage of a known scenario; and

acquire the autonomous driving model according to the sample image ofthe known scenario and the sample image of the target scenario.

Optionally, the acquiring module is specifically configured to:

acquire the sample image of the target scenario according to the sampleimage of the known scenario and a pre-trained domain conversion model,where the domain conversion model is used to indicate a relationshipbetween an image of the known scenario and an image of the targetscenario.

Optionally, the sample image of the known scenario is a daytime sampleimage, and the sample image of the target scenario is a night sampleimage; and the acquiring module is specifically configured to:

acquire the night sample image according to the daytime sample image andthe pre-trained domain conversion model, where the domain conversionmodel is used to indicate a relationship between a daytime image and anight image.

Optionally, the sample image of the known scenario is a sunny-day sampleimage, and the sample image of the target scenario is a rainy-day sampleimage; and the acquiring module is specifically configured to:

acquire the rainy-day sample image according to the sunny-day sampleimage and the pre-trained domain conversion model, where the domainconversion model is used to indicate a relationship between a sunny-dayimage and a rainy-day image.

Optionally, the acquiring module is specifically configured to:

train a model with a generative adversarial network (GAN) technique toobtain the domain conversion model.

Optionally, the sample image of the known scenario is a sample image inwhich there is no pedestrian and vehicle in front of a vehicle, and thesample image of the target scenario is a sample image in which there isa pedestrian and a vehicle in front of the vehicle; and the acquiringmodule is specifically configured to:

perform vehicle marking and pedestrian marking on the sample image inwhich there is no pedestrian and vehicle in front of the vehicle toobtain the sample image in which there is a pedestrian and a vehicle infront of the vehicle.

In a third aspect, the present disclosure provides a computer readablestorage medium having a computer program stored thereon, where thecomputer program implements the autonomous driving method describedabove when executed by a processor.

In a fourth aspect, the present disclosure provides a server, including:

a processor; and

a memory, configured to store an executable instruction of theprocessor;

where the processor is configured to implement the autonomous drivingmethod described above via executing the executable instruction.

In the autonomous driving method provided in the present disclosure, aserver first receives a currently collected image sent by a unmannedvehicle, where the currently collected image herein is an imagecollected in a target scenario, the server then acquires current drivingdata according to the currently collected image and a pre-trainedautonomous driving model, and returns the driving data to the unmannedvehicle so that the unmanned vehicle can travel in at least twoscenarios, thereby improving the robustness of the unmanned drivingmethod.

BRIEF DESCRIPTION OF DRAWING(S)

In order to illustrate technical solutions in embodiments of the presentdisclosure or the prior art more clearly, accompanying drawings used fordescription of the embodiments of the present disclosure or the priorart will be briefly described hereunder. Obviously, the describeddrawings are merely some embodiments of present disclosure. For personsof ordinary skilled in the art, other drawings may be obtained based onthese drawings without any creative effort.

FIG. 1 is an application scenario diagram of an autonomous drivingmethod according to the present disclosure;

FIG. 2 is a schematic flow chart of Embodiment 1 of the autonomousdriving method according to the present disclosure;

FIG. 3 is a schematic flow chart of Embodiment 2 of the autonomousdriving method according to the present disclosure;

FIG. 4 is a schematic diagram of acquiring driving data according to thepresent disclosure;

FIG. 5 is a schematic flow chart of Embodiment 3 of the autonomousdriving method according to the present disclosure;

FIG. 6 is another schematic diagram of acquiring driving data accordingto the present disclosure;

FIG. 7 is a schematic flow chart of Embodiment 4 of the autonomousdriving method according to the present disclosure;

FIG. 8 is still another schematic diagram of acquiring driving dataaccording to the present disclosure;

FIG. 9 is a schematic structural diagram of an autonomous drivingapparatus according to the present disclosure; and

FIG. 10 is a schematic structural diagram of hardware of a serveraccording to the present disclosure.

DESCRIPTION OF EMBODIMENTS

In order to make objectives, technical solutions, and advantages ofembodiments of the present disclosure clearer, the technical solutionsin the embodiments of the present disclosure will be described hereunderclearly and comprehensively with reference to the accompanying drawingsin the embodiments of the present disclosure. Obviously, the describedembodiments are only a part of embodiments of the present disclosure,rather than all embodiments of the present disclosure. All otherembodiments obtained by persons of ordinary skilled in the art based onthe embodiments of the present disclosure without any creative effortshall fall into the protection scope of the present disclosure.

Terms such as “first”, “second”, “third”, “fourth” (if present) in thespecification and the claims as well as the described accompany drawingsof the present disclosure are used to distinguish similar objects, butnot intended to describe a specific order or sequence. It will beappreciated that the data used in this way can be interchangeable underappropriate circumstances, such that the embodiments of the presentdisclosure described herein can be implemented, for instance, in anorder other than those illustrated or described herein. Moreover, termssuch as “include” and “comprise” and any variation thereof are intendedto cover a non-exclusive inclusion, for example, processes, methods,systems, products or devices that encompass a series of steps or unitsare not necessarily limited to those steps or units that are clearlylisted, but may include other steps or units that are not explicitlylisted or inherent to these processes, methods, products or devices.

In the prior art, the end-to-end autonomous driving method needs to beachieved through processes such as data collecting and model training,which has the problem that when the collected data is data collected ina certain scenario, a model obtained from training based on the data canonly be used in this scenario, and if the autonomous vehicle is inanother scenario, the model cannot produce appropriate output. Forexample, when the collected data is daytime data, a model obtained fromtraining cannot be used at night; for another example, when thecollected data does not have data at a rainy time, a model obtained fromtraining cannot be used when it rains; for still another example, whenthe collected data is data indicating that there is no vehicle orpedestrian in the front, a model obtained from training cannot be usedin a case where there is a vehicle or a pedestrian in the front. Theabove-mentioned autonomous driving method provided in the prior art hasa limited application scenario and low robustness.

Based on the above technical problem, the present disclosure provides anautonomous driving method and an apparatus. An autonomous driving modelthat can be used in at least two scenarios is obtained from trainingUpon reception of a currently collected image sent by a unmannedvehicle, the image is input into the above-mentioned autonomous drivingmodel so that corresponding driving data can be obtained, and for theautonomous vehicle, driving safety can be improved based on the drivingdata.

FIG. 1 is an application scenario diagram of an autonomous drivingmethod according to the present disclosure. The application scenariodiagram shown in FIG. 1 includes: autonomous vehicles and a server.

Where the above-mentioned autonomous vehicle is equipped with an imagecollecting device. Optionally, the image collecting device can beinstalled on a front windshield of the autonomous vehicle, which is usedto capture an image in front of the autonomous vehicle. The imagecollecting device can be any device that can achieve an image collectingfunction, such as a webcam, a video camera or a camera.

The server may be connected to N autonomous vehicles simultaneously.After the image collecting device sends the captured image in front ofthe autonomous vehicle to the server, the server can calculate currentdriving data according to the image and a pre-trained autonomous drivingmodel, and return the driving data to the autonomous vehicle so that theautonomous vehicle travels based on the driving data.

A detailed illustration will be given hereunder on the technicalsolutions of the present disclosure and how the above technical problemis solved using the technical solutions of the present disclosure withreference to specific embodiments. The specific embodiments below can becombined with each other, and for the same or similar concepts orprocesses, details may not be described in some embodiments for the sakeof redundancy. The embodiments of the present disclosure will bedescribed hereunder with reference to the accompanying drawings.

FIG. 2 is a schematic flow chart of Embodiment 1 of the autonomousdriving method according to the present disclosure. The autonomousdriving method provided in this embodiment can be performed by theserver shown in FIG. 1. As shown in FIG. 2, the autonomous drivingmethod provided in this embodiment includes:

S201: receiving a currently collected image sent by an unmanned vehicle,where the currently collected image is an image collected in a targetscenario.

Where, as described above, an image may be collected by an imagecollecting device installed on the front windshield of the unmannedvehicle. The currently collected image described above refers to animage in front of the unmanned vehicle.

S202: acquiring current driving data according to the currentlycollected image and a pre-trained autonomous driving model.

S203: sending the current driving data to the unmanned vehicle.

Where the above pre-trained autonomous driving model is used to indicatea relationship between an image and driving data in at least twoscenarios at the same place, and the at least two scenarios hereininclude the target scenario in S101. The scenarios here include:daytime, night, sunny day, rainy day, a scenario where there is avehicle and a pedestrian in front of the vehicle, and a scenario wherethere is no vehicle and pedestrian in front of the vehicle.

Optionally, time periods corresponding to the daytime and the nightdescribed above can be artificially defined, and the sunny day and therainy day described above can be subject to the weather forecast.

Specifically, the pre-trained autonomous driving model as describedabove can be acquired by: firstly, generating a sample image of thetarget scenario according to a sample image of the known scenario; andthen acquiring the autonomous driving model according to the sampleimage of the known scenario and the sample image of the target scenario.

For example, assuming that the pre-trained autonomous driving model asdescribed above refers to a relationship between an image and drivingdata in two scenarios at daytime and night, and the currently collectedimage in S201 is a night image, then driving data of a correspondingplace at night can be output after the night image collected in S201 isinput into the autonomous driving model described above.

Where the driving data includes a traveling speed of the vehicle, asteering wheel angle, and the like. Since the autonomous driving modelin this embodiment is applicable to at least two scenarios, theautonomous driving method in this embodiment can be used in at least twoscenarios and driving safety of the autonomous driving vehicle isimproved.

According to the autonomous driving method provided in this embodiment,a server firstly receives a currently collected image sent by a unmannedvehicle, where the currently collected image herein is an imagecollected in a target scenario, the server then acquires current drivingdata according to the currently collected image and a pre-trainedautonomous driving model, and returns the driving data to the unmannedvehicle so that the unmanned vehicle can travel in at least twoscenarios, thereby improving robustness of the unmanned driving method.

It can be seen from the above description that the unmanned drivingmethod according to the present disclosure is implemented based on anautonomous driving model, and a detailed description will be givenhereunder on how to acquire the above-described autonomous driving modelin conjunction with a specific embodiment. Specifically, it is dividedinto the following three cases:

Case 1: the known scenario differs from the target scenario in terms oftime periods.

Case 2: the known scenario differs from the target scenario in terms ofweather.

Case 3: the known scenario differs from the target scenario in terms ofwhether there is a pedestrian and a vehicle in front of the vehicle.

FIG. 3 is a schematic flow chart of Embodiment 2 of the autonomousdriving method according to the present disclosure. This embodiment anillustration of the autonomous driving method described above in Case 1.In this embodiment, the sample image of the known scenario is a daytimesample image; and the sample image of the target scenario is a nightsample image. As shown in FIG. 3, the autonomous driving method providedin this embodiment includes:

S301: receiving a currently collected image sent by an unmanned vehicle,where the currently collected image is an image collected at night.

S302: acquiring a night sample image according to a daytime sample imageand a pre-trained domain conversion model, where the domain conversionmodel is used to indicate a relationship between a daytime image and anight image.

Where the domain conversion model is a pre-trained model for convertingan image of one time period into an image of another time period, whichis used here to convert the daytime sample image into the night sampleimage.

Specifically, a process for training the above-mentioned domainconversion model can be: collecting daytime sample data andcorresponding night sample data, and training the daytime sample dataand the night sample data to obtain a domain conversion model fromdaytime to nighttime.

Optionally, the domain conversion model can be obtained from trainingwith a generative adversarial network (GAN) technique.

S303: acquiring the autonomous driving model according to the daytimesample image and the night sample image.

S304: acquiring current driving data according to the currentlycollected image and a pre-trained autonomous driving model.

S305: sending the current driving data to the unmanned vehicle.

A principle for acquiring driving data in this embodiment will bedescribed hereunder with reference to FIG. 4:

daytime sample data and corresponding night sample data are collected,and the daytime sample data and the night sample data are trained toobtain a daytime→night domain conversion model. A daytime sample imageis input into the domain conversion model to obtain a night sampleimage. A training is performed based on the daytime sample image and thenight sample image so that an autonomous driving model is obtained, andthe currently collected night image is input into the autonomous drivingmodel to obtain current driving data.

The autonomous driving method according to this embodiment illustratesthe autonomous driving method described above in Case 1. This method isapplicable to both daytime and nighttime, and thus robustness of theunmanned driving method is improved.

FIG. 5 is a schematic flow chart of a third embodiment of the autonomousdriving method according to the present disclosure. This embodimentillustrates the autonomous driving method described above in Case 2. Inthis embodiment, the sample image of the known scenario is a sunny-daysample image; and the sample image of the target scenario is a rainy-daysample image. As shown in FIG. 5, the autonomous driving method providedin this embodiment includes:

S501: receiving a currently collected image sent by an unmanned vehicle,where the currently collected image is an image collected at rainy day.

S502: acquiring a rainy-day sample image according to a sunny-day sampleimage and a pre-trained domain conversion model, where the domainconversion model is used to indicate a relationship between a sunny-dayimage and a rainy-day image.

Where the domain conversion model is a pre-trained model for convertingan image of one kind of weather into an image of another kind ofweather, which is used here to convert the sunny-day sample image intothe rainy-day sample image.

Specifically, a process for training the above-mentioned domainconversion model can be: collecting sunny-day sample data andcorresponding rainy-day sample data, and training the sunny-day sampledata and the rainy-day sample data to obtain a domain conversion modelfrom sunny day to rainy day.

S503: acquiring the autonomous driving model according to the sunny-daysample image and the rainy-day sample image.

S504: acquiring current driving data according to the currentlycollected image and a pre-trained autonomous driving model.

S505: sending the current driving data to the unmanned vehicle.

A process for acquiring driving data in this embodiment will bedescribed hereunder with reference to FIG. 6:

Sunny-day sample data and corresponding rainy-day sample data arecollected, and the sunny-day sample data and the rainy-day sample dataare trained to obtain a sunny-day→rainy-day domain conversion model. Asunny-day sample image is input into the domain conversion model toobtain a rainy-day sample image. A training is performed based on thesunny-day sample image and the rainy-day sample image so that anautonomous driving model is obtained, and the currently collectedrainy-day image is input into the autonomous driving model to obtaincurrent driving data.

The autonomous driving method provided in this embodiment illustratesthe autonomous driving method described above in Case 2. This method isapplicable to both sunny day and rainy day, and thus robustness of theunmanned driving method is improved.

FIG. 7 is a schematic flow chart of Embodiment 4 of the autonomousdriving method according to the present disclosure. This embodiment isan illustration of the autonomous driving method described above in Case3. In this embodiment, the sample image of the known scenario is asample image in which there is no pedestrian and vehicle in front of thevehicle; and the sample image of the target scenario is a sample imagein which there is a pedestrian and a vehicle in front of the vehicle. Asshown in FIG. 8, the autonomous driving method provided in thisembodiment includes:

S701: receiving a currently collected image sent by a unmanned vehicle,where the currently collected image is an image in which there is apedestrian and/or a vehicle in front of the vehicle.

S702: performing vehicle marking and/or pedestrian marking on a sampleimage in which there is no pedestrian and vehicle in front of thevehicle to obtain the sample image in which there is a pedestrian and/ora vehicle in front of the vehicle.

Specifically, the sample image in which there is no pedestrian andvehicle in front of the vehicle can be marked with a vehicle and/orpedestrian to simulate the image in which there is a pedestrian and/or avehicle in front of the vehicle, and the marking method can utilize animage synthesis technology. The location and the number of vehicles andpedestrians can be flexibly set as desired.

S703: acquiring the autonomous driving model according to the sampleimage in which there is no pedestrian and vehicle in front of thevehicle and the sample image in which there is a pedestrian and/or avehicle in front of the vehicle.

S704: acquiring current driving data according to the currentlycollected image and a pre-trained autonomous driving model.

S705: sending the current driving data to the unmanned vehicle.

A process for acquiring driving data in this embodiment will bedescribed hereunder with reference to FIG. 8:

an existing sample image in which there is no pedestrian and vehicle infront of the vehicle is marked with a vehicle and/or a pedestrian toobtain a sample image in which there is a pedestrian and/or a vehicle infront of the vehicle. A training is performed based on the sample imagein which there is no pedestrian and vehicle in front of the vehicle andthe sample image in which there is a pedestrian and/or a vehicle infront of the vehicle, so that an autonomous driving model is obtained,and the currently collected night image is input into the autonomousdriving model to obtain current driving data.

The autonomous driving method provided in this embodiment illustratesthe autonomous driving method described above in Case 3. This method isapplicable to either a case where there is a vehicle and a pedestrian infront of the vehicle or a case where there is no vehicle and pedestrianin front of the vehicle, and thus robustness of the unmanned drivingmethod is improved.

FIG. 9 is a schematic structural diagram of an autonomous drivingapparatus according to the present disclosure. As shown in FIG. 9, theautonomous driving apparatus provided in the present disclosureincludes:

a receiving module 901, configured to receive a currently collectedimage sent by a unmanned vehicle, where the currently collected image isan image collected in a target scenario;

an acquiring module 902, configured to acquire current driving dataaccording to the currently collected image and a pre-trained autonomousdriving model, where the autonomous driving model is configured toindicate a relationship between an image and driving data in at leasttwo scenarios, and the at least two scenarios include the targetscenario; and

a sending module 903, configured to send the current driving data to theunmanned vehicle.

Optionally, the acquiring module 902 is further configured to:

generate a sample image of the target scenario according to a sampleimage of a known scenario; and

acquire the autonomous driving model according to the sample image ofthe known scenario and the sample image of the target scenario.

Optionally, the acquiring module 902 is specifically configured to:

acquire the sample image of the target scenario according to the sampleimage of the known scenario and a pre-trained domain conversion model,where the domain conversion model is used to indicate a relationshipbetween an image of the known scenario and an image of the targetscenario.

Optionally, the sample image of the known scenario is a daytime sampleimage, and the sample image of the target scenario is a night sampleimage; and the acquiring module 902 is specifically configured to:

acquire the night sample image according to the daytime sample image andthe pre-trained domain conversion model, where the domain conversionmodel is used to indicate a relationship between a daytime image and anight image.

Optionally, the sample image of the known scenario is a sunny-day sampleimage, and the sample image of the target scenario is a rainy-day sampleimage; and the acquiring module 902 is specifically configured to:

acquire the rainy-day sample image according to the sunny-day sampleimage and the pre-trained domain conversion model, where the domainconversion model is used to indicate a relationship between a sunny-dayimage and a rainy-day image.

Optionally, the acquiring module 902 is specifically configured to:

train a model with a generative adversarial network (GAN) technique toobtain the domain conversion model.

Optionally, the sample image of the known scenario is a sample image inwhich there is no pedestrian and vehicle in front of the vehicle, andthe sample image of the target scenario is a sample image in which thereis a pedestrian and a vehicle in front of the vehicle; and the acquiringmodule 902 is specifically configured to:

perform vehicle marking and pedestrian marking on the sample image inwhich there is no pedestrian and vehicle in front of the vehicle toobtain the sample image in which there is a pedestrian and a vehicle infront of the vehicle.

The autonomous driving apparatus provided in this embodiment can be usedto perform the autonomous driving method described above in any one ofthe embodiments; and implementation principles and technical effectsthereof are similar, for which details will not be described hereinagain.

FIG. 10 is a schematic structural diagram of hardware of a serveraccording to the present disclosure. As shown in FIG. 10, the server inthis embodiment can include:

a memory 1001, configured to store a program instruction; and

a processor 1002, configured to implement the autonomous driving methoddescribed above in any one of the embodiments when the programinstruction is executed. Reference may be made to the previousembodiment for a detailed implementation principle, which will not bedescribed herein again in this embodiment.

The present disclosure provides a computer readable storage mediumhaving a computer program stored thereon, where the computer programimplements the autonomous driving method described above in any one ofthe embodiments when executed by a processor.

The present disclosure also provides a program product, where theprogram product includes a computer program stored in a readable storagemedium. At least one processor can read the computer program from thereadable storage medium, and the at least one processor executes thecomputer program such that a server implements the autonomous drivingmethod described above in any one of the embodiments.

In several embodiments provided in the present disclosure, it should beunderstood that the disclosed apparatus and method can be implemented inother manners. For example, the described apparatus embodiments aremerely exemplary. For example, the division of the units is merely adivision of logical functions and there can be other division mannersduring actual implementations. For example, a plurality of units orcomponents can be combined or integrated into another system, or somefeatures can be omitted or not performed. For another, the mutualcoupling or direct coupling or a communication connection shown ordiscussed can be indirect coupling or a communication connection viasome interfaces, devices or units, and can be electrical, mechanical orin other forms.

The units described as separate components can be or cannot bephysically separate, and components shown as units can be or cannot bephysical units, that is, can be located in one position, or can bedistributed on a plurality of network units. A part or all of the unitscan be selected according to actual needs to achieve the purpose of thesolution of the present embodiment.

In addition, each functional unit in each embodiment of the presentdisclosure can be integrated into one processing unit, or each of theunits can exist alone physically, or two or more units are integratedinto one unit. The above integrated units can be implemented in a formof hardware or in a form of hardware plus software functional units.

The integrated unit implemented in the form of software functional unitas described above can be stored in a computer readable storage medium.The above software functional unit is stored in a storage medium, andincludes several instructions for enabling a computer device (which canbe a personal computer, a server, or a network device, etc.) or aprocessor to perform a part of steps of the method described in eachembodiment of the present disclosure. The foregoing storage mediumincludes: any medium that can store program codes, such as a USB flashdisk, a mobile hard disk, a read-only memory (ROM for short), a randomaccess memory (RAM for short), a magnetic disk, or an optical disc, etc.

In embodiments of the network device or the terminal device describedabove, it will be appreciated that the processor may be a centralprocessing unit (CPU for short), or other general purpose processor,digital signal processor (DSP for short), application specificintegrated circuit (ASIC for short), etc. The general purpose processorcan be a microprocessor; alternatively, the processor can also be anyconventional processor or the like. The steps of the method disclosed inconnection with the present disclosure can be directly embodied as beingperformed and accomplished by a hardware processor or performed andaccomplished by a combination of hardware and software modules in aprocessor.

Finally, it should be noted that each of the above embodiments is merelyintended for describing the technical solutions of the presentdisclosure, rather than limiting the present disclosure. Although thepresent disclosure is described in detail with reference to theforegoing embodiments, persons of ordinary skilled in the art shouldunderstand that they can still make modifications to the technicalsolutions described in each of the foregoing embodiments, or makeequivalent substitutions to some or all technical features therein;however, these modifications or substitutions do not make the essence ofcorresponding technical solutions depart from the scope of the technicalsolutions of the embodiments of the present disclosure.

What is claimed is:
 1. An autonomous driving method, comprising:receiving a currently collected image sent by an unmanned vehicle,wherein the currently collected image is an image collected in a targetscenario; acquiring current driving data according to the currentlycollected image and a pre-trained autonomous driving model, wherein theautonomous driving model is used to indicate a relationship between animage and driving data in at least two scenarios, and the at least twoscenarios comprise the target scenario; and sending the current drivingdata to the unmanned vehicle.
 2. The method according to claim 1, beforethe acquiring current driving data according to the currently collectedimage and a pre-trained autonomous driving model, comprising: generatinga sample image of the target scenario according to a sample image of aknown scenario; and acquiring the autonomous driving model according tothe sample image of the known scenario and the sample image of thetarget scenario.
 3. The method according to claim 2, wherein thegenerating a sample image of the target scenario according to a sampleimage of a known scenario comprises: acquiring the sample image of thetarget scenario according to the sample image of the known scenario anda pre-trained domain conversion model, wherein the domain conversionmodel is used to indicate a relationship between an image of the knownscenario and an image of the target scenario.
 4. The method according toclaim 3, wherein the sample image of the known scenario is a daytimesample image, and the sample image of the target scenario is a nightsample image; and the acquiring the sample image of the target scenarioaccording to the sample image of the known scenario and a pre-traineddomain conversion model comprises: acquiring the night sample imageaccording to the daytime sample image and the pre-trained domainconversion model, wherein the domain conversion model is used toindicate a relationship between a daytime image and a night image. 5.The method according to claim 3, wherein the sample image of the knownscenario is a sunny-day sample image, and the sample image of the targetscenario is a rainy-day sample image; and the acquiring the sample imageof the target scenario according to the sample image of the knownscenario and a pre-trained domain conversion model comprises: acquiringthe rainy-day sample image according to the sunny-day sample image andthe pre-trained domain conversion model, wherein the domain conversionmodel is used to indicate a relationship between a sunny-day image and arainy-day image.
 6. The method according to claim 3, before theacquiring the sample image of the target scenario according to thesample image of the known scenario and a pre-trained domain conversionmodel, further comprising: training a model with a generativeadversarial network (GAN) technique to obtain the domain conversionmodel.
 7. The method according to claim 2, wherein the sample image ofthe known scenario is a sample image in which there is no pedestrian andvehicle in front of a vehicle, and the sample image of the targetscenario is a sample image in which there is a pedestrian and a vehiclein front of the vehicle; and the generating a sample image of the targetscenario according to a sample image of a known scenario comprises:performing vehicle marking and pedestrian marking on the sample image inwhich there is no pedestrian and vehicle in front of the vehicle toobtain the sample image in which there is a pedestrian and a vehicle infront of the vehicle.
 8. An autonomous driving apparatus, comprising: aprocessor; and a memory, configured to store an executable instructionof the processor; wherein the executable instruction, when executed bythe processor, causes the processor to: receive a currently collectedimage sent by an unmanned vehicle, wherein the currently collected imageis an image collected in a target scenario; acquire current driving dataaccording to the currently collected image and a pre-trained autonomousdriving model, wherein the autonomous driving model is configured toindicate a relationship between an image and driving data in at leasttwo scenarios, and the at least two scenarios comprise the targetscenario; and send the current driving data to the unmanned vehicle. 9.A nonvolatile memory having a computer program stored thereon, whereinthe computer program implements the method according to of claim 1 whenexecuted by a processor.